I have a large set of photographs I made while running. They are geotagged, as I took them with my phone camera. The compass direction is completely unreliable, but lat/lon is more trustworthy. I thought it would be an interesting experiment to extract greenery like grass and trees from these photographs. It can be a useful addition for creating routes that are more pleasant to walk, since the eye-level point of view is not available in OSM. As this is based on my personal photographs, it has the additional benefit of recommending routes that I tend to use. The first challenge I encountered is that out of a few thousand photographs, only a handful were taken during the daytime. After deduplicating and dropping all photos that contain no greenery, this becomes a relatively small set of waypoints. I decided not to extrapolate additional points along OSM ways to keep the dataset small and avoid adding misleading info. The greenery detection works well enough with the SegFormer model, although it is somewhat slow locally. My plan is to select waypoints from this dataset before calling OSRM. This way I get routes that are more enjoyable to walk and run, but are generally longer than the default shortest route. You can find my dataset on Kaggle.

A few quick notes on some changes I made to OSM based on local knowledge.

1. Changed the point for the Riverside Centre building to reflect that it is now a Builder’s Corner hardware store.

2. Added a point for the nearby Hole in the Wall Centre

3. Defined an area for the Somerset Lofts apartment complex and added some details for it.

I’ve recently begun contributing street-level imagery on Mapillary and Panoramax in my local area. I figured that my dash cam was already recording anyway, so if it could be of use to anyone, why not share it?

Contributing to Mapillary was very easy; since my dash cam has an integrated GPS that encoded its data into the video file, I could just upload the video to Mapillary and their website would turn it into an image sequence. Panoramax requires you to preprocess the video into geotagged images yourself, which made it hard to contribute to. Some cameras can be configured to save periodic images instead of videos, but that didn’t work for me because I still needed the dash cam to work normally as a dash cam first and Panoramax instrument second. It took me a while to figure it out, so I’m writing this blog post to hopefully help out the next guy in the same situation.

The task involves four basic steps. I scripted a solution that works specifically for my dash cam model (Garmin 47) and operating system (Linux). If Panoramax continues to grow, I imagine that separate scripts could be written for each step to mix and match for different camera types and computing environments. The steps are:

1. Extract the raw GPS data from the dash cam video clip(s)

2. Along the GPS trace, create a set of evenly-spaced points

3. Extract images from the video occurring at the evenly-spaced points, and

4. Add the GPS and time data to the image files

One could go even further and automatically upload the images to Panoramax straight from the terminal, but that’s beyond my coding abilities.

Let’s take a look at each step in detail:

Step 1 - Getting GPS data from the video

Thankfully, Garmin makes this relatively easy to do with exiftool. If you open the terminal in the directory with the video clips and run the command

exiftool GRMN<number>.MP4

The output will contain a warning:

… See full entry

• I spent some time today improving the map data in my local area using the iD editor. As a local, I noticed that several roads were untracted

• added roads but i got confused while selecting presets- then i realised the more i do mapping, the better i will get with using presets. Each preset serves a unique purpose.

• Few weeks ago i spent time mapping my school in my city, i was soo fun- just wish they could use more updated satelite image.

There has been a very interesting question on the OSM US Slack lately.

“Does anyone have a method to search through the OSM database for a building of a particular shape? I need assistance finding OSM buildings with this specific shape. They should be located in NJ, DE, northeastern MD, eastern PA, or southern NY.”

The question quickly exploded into a huge discussion. At the time of writing, there are already 71 replies.

Someone suggested :

“You could load OSM buildings into PostGIS and then use ST_HausdorffDistance to compare the geometries.”

From there, the discussion veered into how to solve that specific puzzle and find the exact OSM building in question.

One person added, “So the strategy is: create the shape of the building you want to search for, scale it to, say, fill a 100x100 m bounding box or something. Ask Postgres to, within a search-area bounding box, take each building and scale it to a 100x100 m bounding box, compute the Hausdorff distance with the scaled input shape, and return all OSM element IDs and their Hausdorff distances, sorted in ascending order.”

Another said, “What I’m currently doing is combining several shape exports into a single file with around 20,000 objects that have concavity. Concavity plus more than 10 nodes eliminates most buildings.”

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At that point, instead of hunting that elusive specific OSM building, I became more interested in the generalized version of the problem.

So I added my two cents to the discussion:

“The generalized version of this problem would be : Can we represent a shape in some kind of data type that allows us to computationally check whether two objects have the same shape, regardless of rotation and scaling?

I haven’t studied the Hausdorff distance yet, but I’m wondering whether it can solve this problem, or if there’s a better alternative—Hu moments, Procrustes analysis, Fourier descriptors for contours…”

Someone replied :

… See full entry

Changeset: 178729012

Today I contributed to OpenStreetMap by improving map completeness in my local area in Bengaluru, Karnataka.

🔹 What I Worked On

Added a missing café using local knowledge Verified placement to ensure it was mapped at the correct entrance location Added appropriate tags including: amenity=cafe name= ##Bean Stop Café

Checked for duplicate entries before uploading

🔹 Mapping Approach

I focused only on verified, ground-truth information and avoided copying from copyrighted sources. All additions were based on direct familiarity with the area.

🔹 Quality Checks

Ensured the point was not placed on the roadway Confirmed correct spelling and capitalization Reviewed surrounding features for consistency

🔹 Objective

The goal was to improve local POI completeness and contribute accurate, structured data to OpenStreetMap. This is part of my effort to make consistent, quality-focused contributions rather than large, unverified edits.

Automatic Pedestrian Detection at Signalised Crossings

Hi everyone,

I recently noticed that many modern pedestrian crossings are equipped with automatic detection sensors that trigger the traffic signal without requiring a push button.

Currently, in OpenStreetMap, we can tag:

• highway=crossing and crossing=traffic_signals for signalised crossings
• button_operated=yes/no to indicate if a manual button is present
• traffic_signals:sound=yes/no for auditory signals

However, there is no standard way to indicate automatic activation by a detector for pedestrians or vehicles.

To address this, I have proposed a new tag on the OSM forum: detector_operated=yes/no, which would clearly indicate that a traffic signal is automatically triggered by a detector.

You can view and comment on the proposal here: https://community.openstreetmap.org/t/proposal-tag-traffic-signals-detector-operated-pedestrian-presence-sensor/141624

… See full entry

Changeset: 176210161

In this changeset (176210161), I focused on improving building-level mapping by adding missing building outlines and refining structural details using Bing Maps aerial imagery.

The objective of this session was to enhance spatial accuracy and improve map completeness in the area. I ensured that:

• Building footprints were aligned correctly with satellite imagery
• Proper geometry was maintained
• No duplicate structures were created
• Tagging remained consistent with OSM standards

This edit was completed using the iD editor (v2.37.3), and I requested a review to ensure quality validation and community feedback.

Working on building details helped strengthen my understanding of:

• Accurate polygon tracing
• Satellite imagery interpretation
• Clean data structuring
• Version control within OSM changesets

I will continue improving structured building data and map quality in Karnataka.

Changeset number is 178690672

Today, I worked on improving map data around Yelahanka Taluku, Karnataka. I updated the official name of Sai Vidya Institute of Technology to reflect accurate real-world information and ensured proper tagging consistency.

In addition to correcting the name, I reviewed campus boundary structure, building tagging, and surrounding infrastructure to avoid duplication and maintain data integrity. I verified that the edits align with real-world sources and OSM tagging standards.

My focus during this session was on:

• Accurate name correction
• Structured campus boundary validation
• Road connectivity refinement
• POI accuracy improvement
• Avoiding duplicate objects

This session helped reinforce the importance of precise tagging, version tracking, and reviewing live map data versus cached tiles. I will continue contributing to improving structured geospatial data across Karnataka.

Many people have noticed that publicly available Overpass servers have been suffering from overuse (a typical “tragedy of the commons”). OSM usage policies generally contain the line “OpenStreetMap (OSM) data is free for everyone to use. Our tile servers are not”. Unfortunately, there have been problems with overuse of the public Overpass servers, despite the usage policy. “Just blocking cloud providers” isn’t an option, because (see here - use the translate button below) lots of different sorts of IP addresses, including residential proxy addresses, are the problem.

People who want to use e.g. Overpass Turbo do have the option to point it at a different Overpass API instance. If you’re using Overpass Turbo and you get an error due to unavailability, likely that is because the Overpass API that it is using is overwhelmed. There are other public Overpass API instances, but they may be complete (in terms of geography, or history) or up to date.

… See full entry

Lincolnshire ER OUT Routes

Hello! This is my first Diary Entry and I wanted to dedicate it to the Forum Post that I made about the UKs Only (I Believe) ER OUT routes in the case of any emergencies: mainly flooding in this case.

Overview

After major flooding in 2013 the council created the Lincolnshire ER Routes to enable people to quickly evacuate from the flood areas. Many of you make have driven past these and never even noticed! They are Red rectangular signs with the white text of ER out on them with a direction to follow. They are placed at every turn, so the evacuees follow the road ahead until a signs says otherwise.

Example Sign

Route End

The end of the route signifies that the evacuees are clear of the major flood risk and (presumably) there would be further guidance at the end of the route. The route end sign is the same as the direction signs however it features 5 black diagonal lines.

… See full entry

Purpose

Safe Routes to School is a program designed to help parents encourage their children to walk and bike to school safely, while promoting healthy engagement. In some locations, walking or biking to school is a child’s only means of getting to school. My goal is to help parents find the best routes to school and to assist them in working with local governments to improve safety. But we need more volunteers to help improve the data.

What We’re Building

• Pedestrian and bike maps
• Child-safe routing opetions
• Walking and biking isochrone (walkshed) tools
• Clear tutorials on how to map for pedestrians and cyclists
• Tools SRTS groups can use in their local communities

Pedestrian and bike maps

Bike maps already exist. Not only does OSM offer two different map layers for cyclists, but there are several apps available. Pedestrian maps are a different story. While there are some pedestrian maps, such as the University of Washington’s Taskar Center for Accessible Technology’s AccessMap.app, they are limited. What is needed is a tool that parents can use to find a good walking route to school, as well as a tool that can help improve pedestrian mapping, much like OpenStreetMap US’s OpenTrailMap.us. The rendering should show pedestrian ways, crossings, accessibility features like tactile pads and kerb ramps.

Routing Engines

OSM has some great routing engines. The user interface will likely need to be improved so that features can be added to avoid. For example, railways may be dangerous for children to cross. By allowing the addition of features to be avoid, it’s possible to provide a safe route.

Walking and biking Isochrones (Walksheds)

… See full entry

About 99% of supplements have garbage in them like seed oils, starch, citric acid or other fillers because most people are too ignorant to read the ingredients labels, let alone realize that ultra-processed pseudo-food is harmful for them in the first place.

I wonder if there is some tag for a nutrition supplements store to state that they offer clean supplements with just one ingredient, just like vegetarian tags, except focused on health rather than ethics.

Maybe diet:raw=* could somewhat apply?

For a while, I was interested in understanding what makes one pedestrian OSM way better than another. I wanted to know if there is some generic way to identify good walking routes from OSM data. I looked at Garmin and Strava heatmaps at first. Then I checked Strava segments and their proximity to points of interest such as rivers, ponds, and parks. Then I thought to look at my running pace along OSM ways to separate good and not-so-good walking routes. My idea was simple — a good walking route means a smooth running pace. There are fewer stops, less waiting at intersections, etc. Of course, my pace depends on many factors, such as how far I have to run to get to a certain place. So it cannot be a simple cutoff, but rather the distribution of paces along a given segment. This turned out to be a reasonably good approximation of how good or bad I perceive each route to be. I created this Kaggle dataset as an illustration. This relies on my personal GPX data, so it does not scale, but it captures the kind of local knowledge that I find hard to share in any other way.

I mainly edit OSM based on GPX files from my runs. I use JOSM. I created a simple script to extract a buffered area around my GPX files to make sure I don’t accidentally edit parts of OSM I don’t intend to. See this Github Gist for the source.

Hello! I’m Quincy Morgan. Thank you for considering my candidacy for the board of directors of OpenStreetMap US.

If we haven’t met before, or if you just want to chat about OSM, I encourage you to reach out via OSM, Slack, email, or social media. You can check out my website for detailed information about me, my work, and more ways to contact me.

About

I have been an avid member of the OpenStreetMap community since 2018 in both volunteer and professional capacities. I am the only person to have been employed by both OpenStreetMap US and the OpenStreetMap Foundation. Recently I have organized my activities under Wayside Mapping.

Mapping

As a volunteer mapper, I’ve contributed more than 13,000 changesets over the span of 1,400 mapping days, including more than 3,000 changesets in the past year. My mapping interests are varied but include trails, waterways, peninsulas, scenic routes, monitoring stations, and undermapped places.

Development

As a paid and volunteer open source developer, I have made major contributions to OpenStreetMap tools such as iD, Field Papers, OpenTrailMap, and the openstreetmap.us website. Most recently, I have built and launched themap.is and OSM 411 on my own time.

Life

I’m currently based in New York City but have previously lived in Pennsylvania, California, and Idaho. I’ve spent time in all 48 contiguous United States and all 10 provinces of Canada. When I’m not at my desk, you can find me hiking, biking, paddling, train hopping, or road tripping to the next spot on the map.

Positions

I believe the core roles of a nonprofit board member are threefold:

1. Represent membership

A board member is a democratic representative of the members of the organization. If elected, it would be my job to actively listen to the community and be responsive to their interests.

… See full entry

About me

I started mapping in 2021 as I tried to ride every street in Brooklyn, NY on wandrer.earth. I kept finding private streets and other issues on OSM and began fixing them.

At the moment I split my time between the Seattle area and Tucson, Arizona. My professional work is in non-profit accounting. I have worked with a number of organizations covering a full range of financial responsibilities. Handling everything from hands on bookkeeping to grant management to financial strategy.

My mapping

My most cohesive, complete project is detailed mapping of Train Mountain Railroad, the worlds longest miniature railroad. If you’re curious I gave a talk at State of the Map US in 2024 about it.

Most of my mapping is more miscellaneous in nature. I’m a big StreetComplete user, and I’ll always pull over on a bike ride to map a public bookcase! I also enjoy contributing detail to pedestrian and bicycle networks.

Board service and goals

Through the last two years serving as the Board Treasurer I have help usher OpenStreetMap US through a number of shifts in financial situation. Guiding the staff through financial challenges and changes in financial structure.

Through the next two years I want to continue this work, and assist the staff in streamlining bookkeeping and reporting. In addition, as a best practice I would like to see the organization through a financial audit or review. Ultimately leaving the organization on an even stronger financial foundation than it is today.

The seriousness with which the OpenStreetMap community approaches discussions is a true strength of the project. Over years of debate, the community has formed a highly cohesive tagging system for names. These guidelines are simple for beginners, yet they allow for the precise classification and mapping of real-world names. A name is perhaps the most vital piece of data for most users—it is what they want to see on screens and paper, hear via voice navigation, and even touch.

However, this post isn’t about which tag to choose, but rather the format in which the name should be entered. I want to discuss the phenomenon of ellipsis—or, to put it simply, the shortening of names—and what we as mappers should watch out for when collecting and adding toponyms to the map.

What are elliptical toponyms?

Elliptical toponyms are geographical proper names that have been historically truncated or shortened in oral or written use. These are often names that originally included a generic term (a word indicating the type of object), which over time partially or completely disappeared. The table below shows several examples of such “shortened” Scandinavian and Baltic toponyms:

Historical Full Form	Truncated Form	Explanation
Sed-ezeris	Sedzeris	The hydronym “Lake Seda” lost the generic term ezeris (lake).
Spanggroben	Spanget	A neighborhood near a stream; the second element -groben (ditch/trench) was dropped.
Stampedam	Stampen	A farmstead near a pond; the second element -dam (pond) was dropped, leaving only the specific element.
Sankt Peters Kirche	Sankt Peter	St. Peter’s Church; the second element Kirche (church) was dropped.

… See full entry

https://en.wikipedia.org/wiki/Akkadian_Empire https://en.wikipedia.org/wiki/Ebla#/media/File:Ancient_Near_East_2400BC.svg https://en.wikipedia.org/wiki/Bell_Beaker_culture https://en.wikipedia.org/wiki/Ebla https://www.historicnewburgh.org/_files/ugd/e582e3_718ec184562a4a38b356d24b99a57131.pdf https://www.usi.edu/media/cprjxhjf/campus-maps-floor-plans_01-21-2022.pdf https://amusingartifacts.org/2025/01/20/usi-campus-aerial-views-part-of-the-then-and-now-continuing-series-about-the-march-of-time/

Today, I continued my journey as an OpenStreetMap contributor by actively participating in humanitarian mapping through the HOT Tasking Manager. During this session, I focused on mapping basic features such as buildings and road segments using satellite imagery.

This experience helped me better understand the importance of accurate data in supporting humanitarian efforts, including disaster response and community planning. I paid close attention to data quality, proper tagging, and alignment with OpenStreetMap mapping guidelines.

Through this contribution, I strengthened my familiarity with the OSM editing tools and improved my confidence as a mapper. I also gained valuable insight into how collaborative mapping enables volunteers from around the world to support vulnerable communities with reliable geospatial data.

I look forward to continuing my contributions, improving my skills, and participating in more humanitarian mapping projects in the future.